LIME is important, imperfect (like every other ML technique), and vulnerable to adversarial manipulation [1]}. LIME, in its most popular implementation, uses local linear surrogate models fit to perturbed, locally weighted samples to explain regions of machine-learned decision boundaries or response functions [2]}. Like other surrogate models, LIME can be combined with model-specific methods for validation and to yield deeper insights. Consider that Tree SHAP can provide locally accurate and consistent point estimates for local feature importance as in REF  below. LIME can then provide approximate information about modeled local linear trends around the same point. Table REF  contains LIME \(h_{\text{GLM}}\)  coefficients for a local region of a validation set sampled from the UCI credit card data defined by PAY_0 > 1, or customers with a fairly high risk of default due to late most recent payments.Code to replicate Table REF : https://bit.ly/2miCPpo. \(h_{\text{GLM}}\)  models the predictions of a simple interpretable decision tree model, \(g_{\text{tree}}\) , displayed in REF . \(h_{\text{GLM}}\)  coefficients show linear trends between features in the sampled set \(\mathbf {X}_{\text{PAY\_0} > 1}\)  and \(g_{\text{tree}}(\mathbf {X}_{\text{PAY\_0}> 1})\) . Because \(h_{GLM}\)  is relatively well-fit (0.73 \(R^2\) ) and has a logical intercept (0.77), it can be used along with Shapley values to reason about the modeled average behavior for risky customers, to differentiate the behavior of any one specific risky customer from their peers under the model, or to validate LIME results.
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